A molecular linkage map for Drosophila mediopunctata confirms synteny with Drosophila melanogaster and suggests a region that controls the variation in the number of abdominal spots

The classic approach to gene discovery relies on the construction of linkage maps. We report the first molecular-based linkage map for Drosophila mediopunctata, a neotropical species of the tripunctata group. Eight hundred F2 individuals were genotyped at 49 microsatellite loci, resulting in a map that is approximate to 450 centimorgans long. Five linkage groups were detected, and the species' chromosomes were identified through cross-references to BLASTn searches and Muller elements. Strong synteny was observed when compared with the Drosophila melanogaster chromosome arms, but little conservation in the gene order was seen. The incorporation of morphological data corresponding to the number of central abdominal spots on the map was consistent with the expected location of a genomic region responsible for the phenotype on the second chromosome.

Exploring the Schistosoma mansoni adult male transcriptome using RNA-seq

Schistosoma mansoni is one of the agents of schistosomiasis, a chronic and debilitating disease. Here we, present a transcriptome-wide characterization of adult S. mansoni males by high-throughput RNA-sequencing. We obtained 1,620,432 high-quality ESTs from a directional strand-specific cDNA library, resulting in a 26% higher coverage of genome bases than that of the public ESTs available at NCBI. With a 15 x-deep coverage of transcribed genomic regions, our data were able to (i) confirm for the first time 990 predictions without previous evidence of transcription; (ii) correct gene predictions; (iii) discover 989 and 1196 RNA-seq contigs that map to intergenic and intronic genomic regions, respectively, where no gene had been predicted before. These contigs could represent new protein-coding genes or non-coding RNAs (ncRNAs). Interestingly, we identified 11 novel Micro-exon genes (MEGs). These data reveal new features of the S. mansoni transcriptional landscape and significantly advance our understanding of the parasite transcriptome. (c) 2011 Elsevier Inc. All rights reserved.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Myostatin: genetic variants, therapy and gene doping

Since its discovery, myostatin (MSTN) has been at the forefront of muscle therapy research because intrinsic mutations or inhibition of this protein, by either pharmacological or genetic means, result in muscle hypertrophy and hyperplasia. In addition to muscle growth, MSTN inhibition potentially disturbs connective tissue, leads to strength modulation, facilitates myoblast transplantation, promotes tissue regeneration, induces adipose tissue thermogenesis and increases muscle oxidative phenotype. It is also known that current advances in gene therapy have an impact on sports because of the illicit use of such methods. However, the adverse effects of these methods, their impact on athletic performance in humans and the means of detecting gene doping are as yet unknown. The aim of the present review is to discuss biosynthesis, genetic variants, pharmacological/genetic manipulation, doping and athletic performance in relation to the MSTN pathway. As will be concluded from the manuscript, MSTN emerges as a promising molecule for combating muscle wasting diseases and for triggering wide-ranging discussion in view of its possible use in gene doping.